{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 机器学习工程师纳米学位\n",
    "## 机器学习基础\n",
    "## 项目 0: 预测泰坦尼克号乘客生还率\n",
    "\n",
    "1912年，泰坦尼克号在第一次航行中就与冰山相撞沉没，导致了大部分乘客和船员身亡。在这个入门项目中，我们将探索部分泰坦尼克号旅客名单，来确定哪些特征可以最好地预测一个人是否会生还。为了完成这个项目，你将需要实现几个基于条件的预测并回答下面的问题。我们将根据代码的完成度和对问题的解答来对你提交的项目的进行评估。 \n",
    "\n",
    "> **提示**：这样的文字将会指导你如何使用 iPython Notebook 来完成项目。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "点击[这里](https://github.com/udacity/machine-learning/blob/master/projects/titanic_survival_exploration/titanic_survival_exploration.ipynb)查看本文件的英文版本。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 了解数据\n",
    "\n",
    "当我们开始处理泰坦尼克号乘客数据时，会先导入我们需要的功能模块以及将数据加载到 `pandas` DataFrame。运行下面区域中的代码加载数据，并使用 `.head()` 函数显示前几项乘客数据。 \n",
    "\n",
    "> **提示**：你可以通过单击代码区域，然后使用键盘快捷键 **Shift+Enter** 或 **Shift+ Return** 来运行代码。或者在选择代码后使用**播放**（run cell）按钮执行代码。像这样的 MarkDown 文本可以通过双击编辑，并使用这些相同的快捷键保存。[Markdown](http://daringfireball.net/projects/markdown/syntax) 允许你编写易读的纯文本并且可以转换为 HTML。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 检查你的Python版本\n",
    "from sys import version_info\n",
    "if version_info.major != 2 and version_info.minor != 7:\n",
    "    raise Exception('请使用Python 2.7来完成此项目')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# 数据可视化代码\n",
    "from titanic_visualizations import survival_stats\n",
    "from IPython.display import display\n",
    "%matplotlib inline\n",
    "\n",
    "# 加载数据集\n",
    "in_file = 'titanic_data.csv'\n",
    "full_data = pd.read_csv(in_file)\n",
    "\n",
    "# 显示数据列表中的前几项乘客数据\n",
    "display(full_data.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从泰坦尼克号的数据样本中，我们可以看到船上每位旅客的特征\n",
    "\n",
    "- **Survived**：是否存活（0代表否，1代表是）\n",
    "- **Pclass**：社会阶级（1代表上层阶级，2代表中层阶级，3代表底层阶级）\n",
    "- **Name**：船上乘客的名字\n",
    "- **Sex**：船上乘客的性别\n",
    "- **Age**:船上乘客的年龄（可能存在 `NaN`）\n",
    "- **SibSp**：乘客在船上的兄弟姐妹和配偶的数量\n",
    "- **Parch**：乘客在船上的父母以及小孩的数量\n",
    "- **Ticket**：乘客船票的编号\n",
    "- **Fare**：乘客为船票支付的费用\n",
    "- **Cabin**：乘客所在船舱的编号（可能存在 `NaN`）\n",
    "- **Embarked**：乘客上船的港口（C 代表从 Cherbourg 登船，Q 代表从 Queenstown 登船，S 代表从 Southampton 登船）\n",
    "\n",
    "因为我们感兴趣的是每个乘客或船员是否在事故中活了下来。可以将 **Survived** 这一特征从这个数据集移除，并且用一个单独的变量 `outcomes` 来存储。它也做为我们要预测的目标。\n",
    "\n",
    "运行该代码，从数据集中移除 **Survived** 这个特征，并将它存储在变量 `outcomes` 中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                               Name  \\\n",
       "0            1       3                            Braund, Mr. Owen Harris   \n",
       "1            2       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2            3       3                             Heikkinen, Miss. Laina   \n",
       "3            4       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4            5       3                           Allen, Mr. William Henry   \n",
       "\n",
       "      Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0    male  22.0      1      0         A/5 21171   7.2500   NaN        S  \n",
       "1  female  38.0      1      0          PC 17599  71.2833   C85        C  \n",
       "2  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3  female  35.0      1      0            113803  53.1000  C123        S  \n",
       "4    male  35.0      0      0            373450   8.0500   NaN        S  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 从数据集中移除 'Survived' 这个特征，并将它存储在一个新的变量中。\n",
    "outcomes = full_data['Survived']\n",
    "data = full_data.drop('Survived', axis = 1)\n",
    "\n",
    "# 显示已移除 'Survived' 特征的数据集\n",
    "display(data.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这个例子展示了如何将泰坦尼克号的 **Survived** 数据从 DataFrame 移除。注意到 `data`（乘客数据）和 `outcomes` （是否存活）现在已经匹配好。这意味着对于任何乘客的 `data.loc[i]` 都有对应的存活的结果 `outcome[i]`。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 计算准确率\n",
    "为了验证我们预测的结果，我们需要一个标准来给我们的预测打分。因为我们最感兴趣的是我们预测的**准确率**，既正确预测乘客存活的比例。运行下面的代码来创建我们的 `accuracy_score` 函数以对前五名乘客的预测来做测试。\n",
    "\n",
    "**思考题**：在前五个乘客中，如果我们预测他们全部都存活，你觉得我们预测的准确率是多少？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions have an accuracy of 60.00%.\n"
     ]
    }
   ],
   "source": [
    "def accuracy_score(truth, pred):\n",
    "    \"\"\" 返回 pred 相对于 truth 的准确率 \"\"\"\n",
    "    \n",
    "    # 确保预测的数量与结果的数量一致\n",
    "    if len(truth) == len(pred): \n",
    "        \n",
    "        # 计算预测准确率（百分比）\n",
    "        return \"Predictions have an accuracy of {:.2f}%.\".format((truth == pred).mean()*100)\n",
    "    \n",
    "    else:\n",
    "        return \"Number of predictions does not match number of outcomes!\"\n",
    "    \n",
    "# 测试 'accuracy_score' 函数\n",
    "predictions = pd.Series(np.ones(5, dtype = int)) #五个预测全部为1，既存活\n",
    "print accuracy_score(outcomes[:5], predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **提示**：如果你保存 iPython Notebook，代码运行的输出也将被保存。但是，一旦你重新打开项目，你的工作区将会被重置。请确保每次都从上次离开的地方运行代码来重新生成变量和函数。\n",
    "\n",
    "### 最简单的预测\n",
    "\n",
    "如果我们要预测泰坦尼克号上的乘客是否存活，但是我们又对他们一无所知，那么最好的预测就是船上的人无一幸免。这是因为，我们可以假定当船沉没的时候大多数乘客都遇难了。下面的 `predictions_0` 函数就预测船上的乘客全部遇难。  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predictions_0(data):\n",
    "    \"\"\" 不考虑任何特征，预测所有人都无法生还 \"\"\"\n",
    "\n",
    "    predictions = []\n",
    "    for _, passenger in data.iterrows():\n",
    "        \n",
    "        # 预测 'passenger' 的生还率\n",
    "        predictions.append(0)\n",
    "    \n",
    "    # 返回预测结果\n",
    "    return pd.Series(predictions)\n",
    "\n",
    "# 进行预测\n",
    "predictions = predictions_0(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**问题1**：对比真实的泰坦尼克号的数据，如果我们做一个所有乘客都没有存活的预测，这个预测的准确率能达到多少？\n",
    "\n",
    "**回答**： *请用预测结果来替换掉这里的文字*\n",
    "\n",
    "**提示**：运行下面的代码来查看预测的准确率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions have an accuracy of 61.62%.\n"
     ]
    }
   ],
   "source": [
    "print accuracy_score(outcomes, predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 考虑一个特征进行预测\n",
    "\n",
    "我们可以使用 `survival_stats` 函数来看看 **Sex** 这一特征对乘客的存活率有多大影响。这个函数定义在名为 `titanic_visualizations.py` 的 Python 脚本文件中，我们的项目提供了这个文件。传递给函数的前两个参数分别是泰坦尼克号的乘客数据和乘客的 生还结果。第三个参数表明我们会依据哪个特征来绘制图形。\n",
    "\n",
    "运行下面的代码绘制出依据乘客性别计算存活率的柱形图。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S4NBGFyZJkrqunoPsJgDLMnNFRHwFuALYouGVSZKkLqtnH/xXM3NJRLwXeD9w\nGfCjxpYlSZK6o56Abz4t7SHAJZl5A142VpKkfq2egJ8XET8GJgI3RsS6dS4nSZL6SD1BfThwM3Bg\nZi4GNsbfwUuS1K/VcxT9K5n5X8CLEbE1MJDy2vCSJKl/quco+vERMQt4Arij/HtTowuTJEldV08X\n/TnAHsD/ZOa2FEfS/6GhVUmSpG6pJ+CXZeYi4E0R8abMvA0Y0+C6JElSN9RzqtrFEbEBcCdwZUQs\noDibnSRJ6qfqacEfCrwC/F/gv4HHgA82sihJktQ97bbgI+JDFJeH/XNm3gxM7pWqJElSt7TZgo+I\nH1K02jcBzomIr/ZaVZIkqVvaa8HvC4wqLzLzZuAuiiPqJUlSP9fePvh/ZOYKKE52A0TvlCRJkrqr\nvRb8uyPiwfJ+ANuVwwFkZo5seHWSJKlL2gv47XutCkmS1KPaDPjM/FtvFiJJknqOl32VJKmCDHhJ\nkiqovd/B31r+/VbvlSNJknpCewfZbR4RewHjI2IKLX4ml5kPNLQySZLUZe0F/NeArwLDgO+2mJbA\nfo0qSpIkdU97R9FPBaZGxFcz0zPYSVKFxFmeu6w35BnZZ9vu8HKxmXlORIynOHUtwO2ZeX1jy5Ik\nSd3R4VH0EXEuMAl4uLxNiohvNrowSZLUdR224IFDgNGZ+TpAREwG/gSc3sjCJElS19X7O/ghNfc3\nbEQhkiSp59TTgj8X+FNE3EbxU7l9gdMaWpUkSeqWeg6y+3lE3A7sVo76YmY+09CqJElSt9TTgicz\n5wPXNbgWSZLUQzwXvSRJFWTAS5JUQe0GfEQ0RcSjvVWMJEnqGe0GfGauAP4aEVv3Uj2SJKkH1HOQ\n3UbAQxFxP7C0eWRmjm9YVZIkqVvqCfivNrwKSZLUo+r5HfwdEbEN8I7M/G1EvBloanxpkiSpq+q5\n2MwnganAj8tRWwLXNrIoSZLUPfX8TO4zwN7ASwCZOQvYrJFFSZKk7qkn4P+emf9oHoiIAUDfXcFe\nkiR1qJ6AvyMiTgfWi4h/Aq4Bft3YsiRJUnfUE/CnAQuBPwMnAjcCX+looYjYKiJui4iHI+KhiJhU\njt84In4TEbPKvxuV4yMiLoyI2RHxYETs0vWHJUnS2q2eo+hfj4jJwH0UXfN/zcx6uuiXA5/LzAci\nYjAwPSJ+AxwL3JqZ50XEaRRfIL4IHAy8o7ztDvyo/CtJkjqpnqPoDwEeAy4ELgJmR8TBHS2XmfMz\n84Hy/hJIIAuhAAAK90lEQVTgEYoj8A8FJpezTQY+VN4/FPjPLPwBGBIRm3fy8UiSJOo70c0FwP/O\nzNkAEbEdcANwU70biYjhwM4UvQBvLS8/C/AM8Nby/pbAnJrF5pbj5teMIyJOAE4A2Hprz6ArSVJr\n6tkHv6Q53EuPA0vq3UBEbAD8AviXzHypdlrZ1d+pI/Iz85LMHJOZY4YOHdqZRSVJWmu02YKPiI+U\nd6dFxI3A1RRhPAH4Yz0rj4iBFOF+ZWb+Vzn62YjYPDPnl13wC8rx84CtahYfVo6TJEmd1F4L/oPl\nbRDwLPA+YCzFEfXrdbTiiAjgMuCRzPxuzaTrgGPK+8cAv6oZ//HyaPo9gBdruvIlSVIntNmCz8zj\nurnuvYGjgT9HxIxy3OnAecDVEXE88Dfg8HLajcAHgNnAK0B3ty9J0lqrw4PsImJb4GRgeO38HV0u\nNjPvBqKNyfu3Mn9SnBZXkiR1Uz1H0V9L0dX+a+D1xpYjSZJ6Qj0B/1pmXtjwSiRJUo+pJ+C/FxFn\nALcAf28e2XwSG0mS1P/UE/A7URwstx9vdNFnOSxJkvqhegJ+AvC22kvGSpKk/q2eM9n9BRjS6EIk\nSVLPqacFPwR4NCL+yKr74Nv9mZwkSeo79QT8GQ2vQpIk9ah6rgd/R28UIkmSek49Z7JbwhtXfFsH\nGAgszcy3NLIwSZLUdfW04Ac33y8vIHMosEcji5IkSd1Tz1H0K2XhWuDABtUjSZJ6QD1d9B+pGXwT\nMAZ4rWEVSZKkbqvnKPoP1txfDjxJ0U0vSZL6qXr2wXtddkmS1jBtBnxEfK2d5TIzz2lAPZIkqQe0\n14Jf2sq49YHjgU0AA16SpH6qzYDPzAua70fEYGAScBwwBbigreUkSVLfa3cffERsDJwKHAVMBnbJ\nzBd6ozBJktR17e2DPx/4CHAJsFNmvtxrVUmSpG5p70Q3nwO2AL4CPB0RL5W3JRHxUu+UJ0mSuqK9\nffCdOsudJEnqPwxxSZIqyICXJKmCDHhJkirIgJckqYIMeEmSKsiAlySpggx4SZIqyICXJKmCDHhJ\nkirIgJckqYIMeEmSKsiAlySpggx4SZIqyICXJKmCDHhJkirIgJckqYIMeEmSKsiAlySpggx4SZIq\nyICXJKmCDHhJkirIgJckqYIMeEmSKsiAlySpggx4SZIqyICXJKmCDHhJkirIgJckqYIMeEmSKsiA\nlySpggx4SZIqyICXJKmCBvR1AVo7xVnR1yWsFfKM7OsSJPURW/CSJFWQAS9JUgU1LOAj4icRsSAi\n/lIzbuOI+E1EzCr/blSOj4i4MCJmR8SDEbFLo+qSJGlt0MgW/OXAQS3GnQbcmpnvAG4thwEOBt5R\n3k4AftTAuiRJqryGBXxm3gk832L0ocDk8v5k4EM14/8zC38AhkTE5o2qTZKkquvtffBvzcz55f1n\ngLeW97cE5tTMN7cct5qIOCEipkXEtIULFzauUkmS1mB9dpBdZibQ6d/wZOYlmTkmM8cMHTq0AZVJ\nkrTm6+2Af7a56738u6AcPw/Yqma+YeU4SZLUBb0d8NcBx5T3jwF+VTP+4+XR9HsAL9Z05UuSpE5q\n2JnsIuLnwFhg04iYC5wBnAdcHRHHA38DDi9nvxH4ADAbeAU4rlF1SZK0NmhYwGfmkW1M2r+VeRP4\nTKNqkSRpbeOZ7CRJqiADXpKkCjLgJUmqIANekqQKMuAlSaogA16SpAoy4CVJqqCG/Q5ekrokoq8r\nWDuc2dcFqNFswUuSVEEGvCRJFWTAS5JUQQa8JEkVZMBLklRBBrwkSRVkwEuSVEEGvCRJFWTAS5JU\nQQa8JEkVZMBLklRBBrwkSRVkwEuSVEEGvCRJFWTAS5JUQQa8JEkVZMBLklRBBrwkSRVkwEuSVEEG\nvCRJFWTAS5JUQQa8JEkVZMBLklRBBrwkSRVkwEuSVEEGvCRJFWTAS5JUQQa8JEkVZMBLklRBBrwk\nSRVkwEuSVEEGvCRJFWTAS5JUQQa8JEkVZMBLklRBBrwkSRVkwEuSVEEGvCRJFWTAS5JUQQa8JEkV\nZMBLklRBBrwkSRVkwEuSVEEGvCRJFWTAS5JUQQa8JEkVZMBLklRBBrwkSRVkwEuSVEH9KuAj4qCI\n+GtEzI6I0/q6HkmS1lT9JuAjogn4AXAwsANwZETs0LdVSZK0Zuo3AQ+8B5idmY9n5j+AKcChfVyT\nJElrpP4U8FsCc2qG55bjJElSJw3o6wI6KyJOAE4oB1+OiL/2ZT3qojP7uoAu2RR4rq+L6Iw4M/q6\nBPVXZ/Z1AV3iZxC2qXfG/hTw84CtaoaHleNWkZmXAJf0VlFSs4iYlplj+roOaW3lZ7Bz+lMX/R+B\nd0TEthGxDnAEcF0f1yRJ0hqp37TgM3N5RHwWuBloAn6SmQ/1cVmSJK2R+k3AA2TmjcCNfV2H1AZ3\nDUl9y89gJ0Rm9nUNkiSph/WnffCSJKmHGPBSF0TE2Ii4vq/rkNYkEXFKRDwSEVc2aP1nRsS/NmLd\na6J+tQ9eklRpnwben5lz+7qQtYEteK21ImJ4RDwaEZdHxP9ExJUR8f6I+H1EzIqI95S3eyPiTxFx\nT0S8q5X1rB8RP4mI+8v5PMWy1EJEXAy8DbgpIr7c2mcmIo6NiGsj4jcR8WREfDYiTi3n+UNEbFzO\n98mI+GNEzIyIX0TEm1vZ3nYR8d8RMT0i7oqId/fuI+57BrzWdm8HLgDeXd4+CrwX+FfgdOBRYJ/M\n3Bn4GvDNVtbxZeB3mfke4H8D50fE+r1Qu7TGyMyTgKcpPiPr0/ZnZkfgI8BuwDeAV8rP373Ax8t5\n/iszd8vMUcAjwPGtbPIS4OTM3JXi8/zDxjyy/ssueq3tnsjMPwNExEPArZmZEfFnYDiwITA5It4B\nJDCwlXUcAIyv2fc3CNia4h+PpNW19ZkBuC0zlwBLIuJF4Nfl+D8DI8v7O0bE14EhwAYU509ZKSI2\nAPYCrolYearYdRvxQPozA15ru7/X3H+9Zvh1is/HORT/cD4cEcOB21tZRwD/nJleF0GqT6ufmYjY\nnY4/kwCXAx/KzJkRcSwwtsX63wQszszRPVv2msUueql9G/LGNRGObWOem4GTo2wqRMTOvVCXtCbr\n7mdmMDA/IgYCR7WcmJkvAU9ExIRy/RERo7pZ8xrHgJfa923g3Ij4E233eJ1D0XX/YNnNf05vFSet\nobr7mfkqcB/we4rjZFpzFHB8RMwEHgLWuoNfPZOdJEkVZAtekqQKMuAlSaogA16SpAoy4CVJqiAD\nXpKkCjLgJbWqPF/4QxHxYETMKE9CImkN4ZnsJK0mIvYExgG7ZObfI2JTYJ0+LktSJ9iCl9SazYHn\nMvPvAJn5XGY+HRG7RsQd5RW6bo6IzSNiQHllr7EAEXFuRHyjL4uX5IluJLWivFjH3cCbgd8CVwH3\nAHcAh2bmwoiYCByYmf8nIkYAU4GTgfOB3TPzH31TvSSwi15SKzLz5YjYFdiH4nKeVwFfp7iU52/K\nU4g3AfPL+R+KiJ8C1wN7Gu5S3zPgJbUqM1dQXD3v9vLyuZ8BHsrMPdtYZCdgMbBZ71QoqT3ug5e0\nmoh4V0S8o2bUaIrr2w8tD8AjIgaWXfNExEeAjYF9ge9HxJDerlnSqtwHL2k1Zff894EhwHJgNnAC\nMAy4kOIyugOAfwd+SbF/fv/MnBMRpwC7ZuYxfVG7pIIBL0lSBdlFL0lSBRnwkiRVkAEvSVIFGfCS\nJFWQAS9JUgUZ8JIkVZABL0lSBRnwkiRV0P8Hgwdhyvi4y6MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13ee110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survival_stats(data, outcomes, 'Sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "观察泰坦尼克号上乘客存活的数据统计，我们可以发现大部分男性乘客在船沉没的时候都遇难了。相反的，大部分女性乘客都在事故中**生还**。让我们以此改进先前的预测：如果乘客是男性，那么我们就预测他们遇难；如果乘客是女性，那么我们预测他们在事故中活了下来。\n",
    "\n",
    "将下面的代码补充完整，让函数可以进行正确预测。  \n",
    "\n",
    "**提示**：您可以用访问 dictionary（字典）的方法来访问船上乘客的每个特征对应的值。例如， `passenger['Sex']` 返回乘客的性别。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predictions_1(data):\n",
    "    \"\"\" 只考虑一个特征，如果是女性则生还 \"\"\"\n",
    "    \n",
    "    predictions = []\n",
    "    for _, passenger in data.iterrows():\n",
    "        \n",
    "        # TODO 1\n",
    "        # 移除下方的 'pass' 声明\n",
    "        # 输入你自己的预测条件\n",
    "        if passenger['Sex'] == 'female':\n",
    "            predictions.append(1)\n",
    "        else:\n",
    "            predictions.append(0)\n",
    "    \n",
    "    # 返回预测结果\n",
    "    return pd.Series(predictions)\n",
    "\n",
    "# 进行预测\n",
    "predictions = predictions_1(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**问题2**：当我们预测船上女性乘客全部存活，而剩下的人全部遇难，那么我们预测的准确率会达到多少？\n",
    "\n",
    "**回答**: *用预测结果来替换掉这里的文字*\n",
    "\n",
    "**提示**：你需要在下面添加一个代码区域，实现代码并运行来计算准确率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions have an accuracy of 78.68%.\n"
     ]
    }
   ],
   "source": [
    "print accuracy_score(outcomes,predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 考虑两个特征进行预测\n",
    "\n",
    "仅仅使用乘客性别（Sex）这一特征，我们预测的准确性就有了明显的提高。现在再看一下使用额外的特征能否更进一步提升我们的预测准确度。例如，综合考虑所有在泰坦尼克号上的男性乘客：我们是否找到这些乘客中的一个子集，他们的存活概率较高。让我们再次使用 `survival_stats` 函数来看看每位男性乘客的年龄（Age）。这一次，我们将使用第四个参数来限定柱形图中只有男性乘客。\n",
    "\n",
    "运行下面这段代码，把男性基于年龄的生存结果绘制出来。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1r32NO+64g6lTpzJhwoSVxr5o0SL22msvpk6dyumnn87EiRNZtGgRANdeey3HHXfcMvOP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\nVHH/P+VriMZB2iSiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13cdd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survival_stats(data, outcomes, 'Age', [\"Sex == 'male'\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "仔细观察泰坦尼克号存活的数据统计，在船沉没的时候，大部分小于10岁的男孩都活着，而大多数10岁以上的男性都随着船的沉没而**遇难**。让我们继续在先前预测的基础上构建：如果乘客是女性，那么我们就预测她们全部存活；如果乘客是男性并且小于10岁，我们也会预测他们全部存活；所有其它我们就预测他们都没有幸存。  \n",
    "\n",
    "将下面缺失的代码补充完整，让我们的函数可以实现预测。  \n",
    "**提示**: 您可以用之前 `predictions_1` 的代码作为开始来修改代码，实现新的预测函数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                               Name  \\\n",
       "0            1       3                            Braund, Mr. Owen Harris   \n",
       "1            2       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2            3       3                             Heikkinen, Miss. Laina   \n",
       "3            4       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4            5       3                           Allen, Mr. William Henry   \n",
       "\n",
       "      Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0    male  22.0      1      0         A/5 21171   7.2500   NaN        S  \n",
       "1  female  38.0      1      0          PC 17599  71.2833   C85        C  \n",
       "2  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3  female  35.0      1      0            113803  53.1000  C123        S  \n",
       "4    male  35.0      0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predictions_2(data):\n",
    "    \"\"\" 考虑两个特征: \n",
    "            - 如果是女性则生还\n",
    "            - 如果是男性并且小于10岁则生还 \"\"\"\n",
    "    \n",
    "    predictions = []\n",
    "    for _, passenger in data.iterrows():\n",
    "        \n",
    "        # TODO 2\n",
    "        # 移除下方的 'pass' 声明\n",
    "        # 输入你自己的预测条件\n",
    "        if passenger['Sex'] == 'female':\n",
    "            predictions.append(1)\n",
    "        elif passenger['Sex'] == 'male' and passenger['Age'] < 10:\n",
    "            predictions.append(1)\n",
    "        else:\n",
    "            predictions.append(0)\n",
    "    \n",
    "    # 返回预测结果\n",
    "    return pd.Series(predictions)\n",
    "\n",
    "# 进行预测\n",
    "predictions = predictions_2(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**问题3**：当预测所有女性以及小于10岁的男性都存活的时候，预测的准确率会达到多少？\n",
    "\n",
    "**回答**: *用预测结果来替换掉这里的文字*\n",
    "\n",
    "**提示**：你需要在下面添加一个代码区域，实现代码并运行来计算准确率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions have an accuracy of 79.35%.\n"
     ]
    }
   ],
   "source": [
    "print accuracy_score(outcomes,predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 你自己的预测模型\n",
    "\n",
    "添加年龄（Age）特征与性别（Sex）的结合比单独使用性别（Sex）也提高了不少准确度。现在该你来做预测了：找到一系列的特征和条件来对数据进行划分，使得预测结果提高到80%以上。这可能需要多个特性和多个层次的条件语句才会成功。你可以在不同的条件下多次使用相同的特征。**Pclass**，**Sex**，**Age**，**SibSp** 和 **Parch** 是建议尝试使用的特征。   \n",
    "\n",
    "使用 `survival_stats` 函数来观测泰坦尼克号上乘客存活的数据统计。  \n",
    "**提示:** 要使用多个过滤条件，把每一个条件放在一个列表里作为最后一个参数传递进去。例如: `[\"Sex == 'male'\", \"Age < 18\"]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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p1zrck4+Iiyj23rcAPhcR5/RbVZIkqdc625M/BBhb3pjmNcCvKVraS5KkQaCz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\nDHlJkirKkJckqaIMeUmSKur/AedSnFIXx5OpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x34c3770>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survival_stats(data, outcomes, 'Age', [\"Sex == 'male'\", \"Age < 18\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                               Name  \\\n",
       "0            1       3                            Braund, Mr. Owen Harris   \n",
       "1            2       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2            3       3                             Heikkinen, Miss. Laina   \n",
       "3            4       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4            5       3                           Allen, Mr. William Henry   \n",
       "\n",
       "      Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0    male  22.0      1      0         A/5 21171   7.2500   NaN        S  \n",
       "1  female  38.0      1      0          PC 17599  71.2833   C85        C  \n",
       "2  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3  female  35.0      1      0            113803  53.1000  C123        S  \n",
       "4    male  35.0      0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1tn9QZu4FvBf4RESMav5gZibldcpbyszJmdmYmY2DBg2qcXGSJPUetaxO/0/g\nSGA5QGbOoThveocyc1H5dwnwc+BdwFMRsR1A+XdJ58uWJEm1hDiZuaDFqDUdzRMRm0dE/6b7wBHA\nX4FpwIRysgnAja23IEmS2lPTIWYRcQCQEdGX4rjxuTXMty3w8/L6432AKzPzlxHxJ+CaiPgI8Hfg\n+HUrXZKk3q2WED8D+C7FtcQXAbcBn+hopsycD4xoZfxy4PDOlSlJklqqZe/0ZXihE0mS3nBq2Tv9\nWxHxpojoGxG3R8TSiPhwVxQnSZLaVsuObUdk5nPAaIrTpL4N+Gw9i5IkSR2rJcSbVrkfDVybmet0\nHnVJkrRh1bJj200R8RDwAvCxiBgEvFjfsiRJUkc67Iln5lnAAUBjZq4GVlGc/1ySJHWjDnviETEW\n+GVmromIc4C9gK9SnPdckqQ2xfnR3SV0iZzU6hnE666WbeLnZubKiDgIeDdwGfCj+pYlSZI6UkuI\nN51i9WhgcmbeTHFZUkmS1I1qCfFFEfFjYBxwS0RsUuN8kiSpjmoJ4+OBXwFHZuYKYCs8TlySpG5X\ny97p/8jMnwHPRsROQF/Ka4tLkqTuU8tpV8dExDzgUeCu8u+t9S5MkiS1r5bV6V8B9gP+X2YOpdhD\n/Y91rUqSJHWolhBfXV4+dKOI2Cgz7wAa61yXJEnqQC2nXV0REVsA04ErImIJxVnbJElSN6qlJ34M\n8A/g/wC/BB4B3l/PoiRJUsfa7YlHxLEUlx79S2b+CpjSJVVJkqQOtdkTj4iLKXrfA4GvRMS5XVaV\nJEnqUHs98VHAiPLCJ5sBd1PsqS5Jkt4A2tsm/lJmroHihC9A77gUjSRJFdFeT/ydEXF/eT+At5bD\nAWRmDq97dZIkqU3thfiuXVaFJEnqtDZDPDP/3pWFSJKkzvGSopIkVZQhLklSRbV3nPjt5d9vdl05\nkiSpVu3t2LZdRBwAjImIqbQ4xCwz76trZZIkqV3thfiXgHOBwcB3WjyWwGH1KkqSJHWsvb3TrwOu\ni4hzM3Odz9QWEQ3ATGBRZo6OiKHAVIrTuc4CxmfmS+vaviRJvVWHO7Zl5lciYkxEXFTeRndyGROB\nuc2Gvwn8R2a+DXgG+Egn25MkSdQQ4hHxDYogfrC8TYyIr9fSeEQMBo4G/qscDorV8NeVk0wBju18\n2ZIkqd1LkZaOBkZm5isAETEF+DNwdg3z/ifwOaB/OTwQWJGZL5fDC4EdWpsxIk4DTgPYaaedaliU\nJEm9S61YvQc6AAAMr0lEQVTHiQ9odv/NtcxQrnZfkpmzOl0VkJmTM7MxMxsHDRq0Lk1IktSj1dIT\n/wbw54i4g+Iws1HAWTXMdyDF4WnvA/oBbwK+CwyIiD5lb3wwsGidKpckqZerZce2q4D9gJ8B1wP7\nZ+bVNcz3hcwcnJlDgBOA32bmScAdwHHlZBOAG9exdkmSerVaeuJk5mJg2gZa5ueBqRHxVYpt65dt\noHYlSepVagrx9ZWZdwJ3lvfnA+/qiuVKktSTeQEUSZIqqt0Qj4iGiHioq4qRJEm1azfEM3MN8HBE\neKC2JElvMLVsE98SeCAi7gVWNY3MzDF1q0qSJHWolhA/t+5VSJKkTuswxDPzrojYGXh7Zv4mIjYD\nGupfmiRJak8tF0A5leKCJT8uR+0A3FDPoiRJUsdqOcTsExSnUH0OIDPnAdvUsyhJktSxWkL8n5n5\nUtNARPQBsn4lSZKkWtQS4ndFxNnAphHxHuBa4Bf1LUuSJHWklhA/C1gK/AU4HbgFOKeeRUmSpI7V\nsnf6KxExBZhBsRr94cx0dbokSd2swxCPiKOBS4BHKK4nPjQiTs/MW+tdnCRJalstJ3v5NvC/M/Nv\nABHxVuBmwBCXJKkb1bJNfGVTgJfmAyvrVI8kSapRmz3xiPhgeXdmRNwCXEOxTXws8KcuqE2SJLWj\nvdXp7292/yngkPL+UmDTulUkSZJq0maIZ+YpXVmIJEnqnFr2Th8KfAoY0nx6L0UqSVL3qmXv9BuA\nyyjO0vZKfcuRJEm1qiXEX8zM79W9EkmS1Cm1hPh3I2IScBvwz6aRmXlf3aqSJEkdqiXE9wDGA4fx\n6ur0LIclSVI3qSXExwJvaX45UkmS1P1qOWPbX4EB9S5EkiR1Ti098QHAQxHxJ167TdxDzCRJ6ka1\nhPikulchSZI6rZbrid/VFYVIkqTOqeWMbSsp9kYH2BjoC6zKzDfVszBJktS+Wnri/ZvuR0QAxwD7\ndTRfRPQDpgOblMu5LjMnladxnQoMBGYB493zXZKkzqtl7/S1snADcGQNk/8TOCwzRwAjgaMiYj/g\nm8B/ZObbgGeAj3SyZkmSRG2r0z/YbHAjoBF4saP5MjOB58vBvuWt6SQxHyrHTwHOA35Uc8WSJAmo\nbe/05tcVfxl4jGKVeociooFilfnbgB8CjwArMvPlcpKFwA5tzHsacBrATjvtVMviJEnqVWrZJr7O\n1xXPzDXAyIgYAPwceGcn5p0MTAZobGzMDiaXJKnXaTPEI+JL7cyXmfmVWheSmSsi4g5gf2BARPQp\ne+ODgUU1VytJktZqb8e2Va3coNgR7fMdNRwRg8oeOBGxKfAeYC5wB3BcOdkE4MZ1qlySpF6uzZ54\nZn676X5E9AcmAqdQHB727bbma2Y7YEq5XXwj4JrMvCkiHgSmRsRXgT8Dl61H/ZIk9VrtbhOPiK2A\nTwMnUexJvldmPlNLw5l5P7BnK+PnA+/qfKmSJKm59raJXwh8kGLnsj0y8/m2ppUkSV2vvW3inwG2\nB84BnoiI58rbyoh4rmvKkyRJbWlvm3inzuYmSZK6lkEtSVJFGeKSJFWUIS5JUkUZ4pIkVZQhLklS\nRRnikiRVlCEuSVJFGeKSJFWUIS5JUkUZ4pIkVZQhLklSRRnikiRVlCEuSVJFGeKSJFWUIS5JUkUZ\n4pIkVZQhLklSRRnikiRVlCEuSVJFGeKSJFWUIS5JUkUZ4pIkVZQhLklSRRnikiRVlCEuSVJFGeKS\nJFVU3UI8InaMiDsi4sGIeCAiJpbjt4qIX0fEvPLvlvWqQZKknqyePfGXgc9k5m7AfsAnImI34Czg\n9sx8O3B7OSxJkjqpbiGemYsz877y/kpgLrADcAwwpZxsCnBsvWqQJKkn65Jt4hExBNgTmAFsm5mL\ny4eeBLZtY57TImJmRMxcunRpV5QpSVKl1D3EI2IL4Hrg3zPzueaPZWYC2dp8mTk5Mxszs3HQoEH1\nLlOSpMqpa4hHRF+KAL8iM39Wjn4qIrYrH98OWFLPGiRJ6qnquXd6AJcBczPzO80emgZMKO9PAG6s\nVw2SJPVkferY9oHAeOAvETG7HHc2cAFwTUR8BPg7cHwda5AkqceqW4hn5u+AaOPhw+u1XEmSegvP\n2CZJUkUZ4pIkVZQhLklSRRnikiRVlCEuSVJFGeKSJFWUIS5JUkUZ4pIkVZQhLklSRRnikiRVlCEu\nSVJFGeKSJFWUIS5JUkUZ4pIkVZQhLklSRRnikiRVlCEuSVJFGeKSJFWUIS5JUkUZ4pIkVZQhLklS\nRRnikiRVlCEuSVJFGeKSJFWUIS5JUkUZ4pIkVZQhLklSRRnikiRVVN1CPCL+OyKWRMRfm43bKiJ+\nHRHzyr9b1mv5kiT1dPXsiV8OHNVi3FnA7Zn5duD2cliSJK2DuoV4Zk4Hnm4x+hhgSnl/CnBsvZYv\nSVJP19XbxLfNzMXl/SeBbbt4+ZIk9RjdtmNbZiaQbT0eEadFxMyImLl06dIurEySpGro6hB/KiK2\nAyj/LmlrwsycnJmNmdk4aNCgLitQkqSq6OoQnwZMKO9PAG7s4uVLktRj1PMQs6uAe4BdImJhRHwE\nuAB4T0TMA95dDkuSpHXQp14NZ+aJbTx0eL2WKUlSb+IZ2yRJqihDXJKkijLEJUmqKENckqSKMsQl\nSaooQ1ySpIoyxCVJqihDXJKkijLEJUmqKENckqSKMsSldRHRO26S3tAMcUmSKsoQlySpogxxSZIq\nyhCXJKmiDHFJkirKEJckqaIMcUmSKsoQlySpogxxSZIqyhCXJKmiemeId/epLD1lpiRpA+idIS5J\nUg9giEuSVFGGuCRJFWWIS5JUUYa4JEkVZYhLklRRhrgkSRXVLSEeEUdFxMMR8beIOKs7apAkqeq6\nPMQjogH4IfBeYDfgxIjYravrkCSp6rqjJ/4u4G+ZOT8zXwKmAsd0Qx2SJFVad4T4DsCCZsMLy3GS\nJKkTIjO7doERxwFHZeZHy+HxwL6Z+ckW050GnFYO7gI83KWFal1tDSzr7iJUaX6GtL56wmdo58wc\n1NFEfbqikhYWATs2Gx5cjnuNzJwMTO6qorRhRMTMzGzs7jpUXX6GtL5602eoO1an/wl4e0QMjYiN\ngROAad1QhyRJldblPfHMfDkiPgn8CmgA/jszH+jqOiRJqrruWJ1OZt4C3NIdy1bduQlE68vPkNZX\nr/kMdfmObZIkacPwtKuSJFWUIa4NIiL+OyKWRMRfu7sWVVNE7BgRd0TEgxHxQERM7O6aVC0R0S8i\n7o2IOeVn6PzurqneXJ2uDSIiRgHPA/9fZu7e3fWoeiJiO2C7zLwvIvoDs4BjM/PBbi5NFRERAWye\nmc9HRF/gd8DEzPxjN5dWN/bEtUFk5nTg6e6uQ9WVmYsz877y/kpgLp7NUZ2QhefLwb7lrUf3VA1x\nSW84ETEE2BOY0b2VqGoioiEiZgNLgF9nZo/+DBnikt5QImIL4Hrg3zPzue6uR9WSmWsycyTF2UDf\nFRE9evOeIS7pDaPcjnk9cEVm/qy761F1ZeYK4A7gqO6upZ4McUlvCOVOSZcBczPzO91dj6onIgZF\nxIDy/qbAe4CHureq+jLEtUFExFXAPcAuEbEwIj7S3TWpcg4ExgOHRcTs8va+7i5KlbIdcEdE3E9x\nnY5fZ+ZN3VxTXXmImSRJFWVPXJKkijLEJUmqKENckqSKMsQlSaooQ1ySpIoyxKVeICLWlIds/TUi\nro2IzdqZ9ryI+L9dWZ+kdWOIS73DC5k5srzC3EvAGd1dkKT1Z4hLvc/dwNsAIuJfI+L+8vrL/9Ny\nwog4NSL+VD5+fVMPPiLGlr36ORExvRw3rLyW8+yyzbd36bOSeiFP9iL1AhHxfGZuERF9KM5N/ktg\nOvBz4IDMXBYRW2Xm0xFxHvB8Zl4UEQMzc3nZxleBpzLz+xHxF+CozFwUEQMyc0VEfB/4Y2ZeEREb\nAw2Z+UK3PGGpl7AnLvUOm5aXZ5wJPE5xjvLDgGszcxlAZrZ2PfjdI+LuMrRPAoaV438PXB4RpwIN\n5bh7gLMj4vPAzga4VH99ursASV3ihfLyjGsV1xvp0OXAsZk5JyJOBg4FyMwzImJf4GhgVkTsnZlX\nRsSMctwtEXF6Zv52Az4HSS3YE5d6r98CYyNiIEBEbNXKNP2BxeUlQk9qGhkRb83MGZn5JWApsGNE\nvAWYn5nfA24Ehtf9GUi9nD1xqZfKzAci4mvAXRGxBvgzcHKLyc4FZlAE9QyKUAe4sNxxLYDbgTnA\n54HxEbEaeBL4et2fhNTLuWObJEkV5ep0SZIqyhCXJKmiDHFJkirKEJckqaIMcUmSKsoQlySpogxx\nSZIqyhCXJKmi/n+0RmTTF6Ob1wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3784470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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nM2/ePN785jevU9bnP/95br/9dubPn8+NN97YaeyrVq1in332Yf78+Zx++unMnj2bVatW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\nmNAlSaoAE7okSRXw/wG2wbsxfw7XEQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3873110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survival_stats(data, outcomes, 'Pclass',[\"Sex == 'female'\", \"Age < 80\"])\n",
    "survival_stats(data, outcomes, 'Age',[\"Sex == 'male'\",\"Pclass == 1\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当查看和研究了图形化的泰坦尼克号上乘客的数据统计后，请补全下面这段代码中缺失的部分，使得函数可以返回你的预测。   \n",
    "在到达最终的预测模型前请确保记录你尝试过的各种特征和条件。   \n",
    "**提示:** 您可以用之前 `predictions_2` 的代码作为开始来修改代码，实现新的预测函数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predictions_3(data):\n",
    "    \"\"\" 考虑多个特征，准确率至少达到80% \"\"\"\n",
    "    \n",
    "    predictions = []\n",
    "    for _, passenger in data.iterrows():\n",
    "        \n",
    "        if passenger['Sex'] == 'female':\n",
    "            if passenger['Age'] > 40 and passenger['Age'] < 60 and passenger['Pclass']==3:\n",
    "                predictions.append(0)\n",
    "            else:\n",
    "                predictions.append(1)\n",
    "            \n",
    "        else:\n",
    "            if passenger['Sex'] == 'male' and passenger['Age']< 10 :            \n",
    "                predictions.append(1)\n",
    "            else:\n",
    "                predictions.append(0)\n",
    "    \n",
    "    # 返回预测结果\n",
    "    return pd.Series(predictions)\n",
    "predictions = predictions_3(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                               Name  \\\n",
       "0            1       3                            Braund, Mr. Owen Harris   \n",
       "1            2       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2            3       3                             Heikkinen, Miss. Laina   \n",
       "3            4       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4            5       3                           Allen, Mr. William Henry   \n",
       "\n",
       "      Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0    male  22.0      1      0         A/5 21171   7.2500   NaN        S  \n",
       "1  female  38.0      1      0          PC 17599  71.2833   C85        C  \n",
       "2  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3  female  35.0      1      0            113803  53.1000  C123        S  \n",
       "4    male  35.0      0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pJqjfBvwYODIzVwLb4+/gJUka0Jo5i/7JzPxv4LGI2A0YQrk3vCRJGpiaOYt+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\nwEuSVEMGvCRJNWTAS5JUQ/8PmrT55+yNOxIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x39fa570>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9gacafhpmZmZWqJgueoBnyG4XexewWNKOjdmJpD7A3sDUVHSepOck/VbS1qms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\nXUTr1C1hM2vj3II3MzPLISd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      "text/plain": [
       "<matplotlib.figure.Figure at 0x3b56fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survival_stats(data, outcomes, 'Parch')\n",
    "survival_stats(data, outcomes, 'SibSp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**问题4**：请描述你实现80%准确度的预测模型所经历的步骤。您观察过哪些特征？某些特性是否比其他特征更有帮助？你用了什么条件来预测生还结果？你最终的预测的准确率是多少？\n",
    "\n",
    "**回答**：*分别分析了性别，年龄和船票等级等数据。女性多数获救但也优先获救孩子和老人，年龄介于40-60之间的女性仍有很多遇难。男性中小孩基本都能获救80.25%*\n",
    "\n",
    "**提示**：你需要在下面添加一个代码区域，实现代码并运行来计算准确率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions have an accuracy of 80.25%.\n"
     ]
    }
   ],
   "source": [
    "print accuracy_score(outcomes,predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 结论\n",
    "\n",
    "经过了数次对数据的探索和分类，你创建了一个预测泰坦尼克号乘客存活率的有用的算法。在这个项目中你手动地实现了一个简单的机器学习模型——决策树（*decision tree*）。决策树每次按照一个特征把数据分割成越来越小的群组（被称为 *nodes*）。每次数据的一个子集被分出来，如果分割后新子集之间的相似度比分割前更高（包含近似的标签），我们的预测也就更加准确。电脑来帮助我们做这件事会比手动做更彻底，更精确。[这个链接](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/)提供了另一个使用决策树做机器学习入门的例子。  \n",
    "\n",
    "决策树是许多**监督学习**算法中的一种。在监督学习中，我们关心的是使用数据的特征并根据数据的结果标签进行预测或建模。也就是说，每一组数据都有一个真正的结果值，不论是像泰坦尼克号生存数据集一样的标签，或者是连续的房价预测。\n",
    "\n",
    "**问题5**：想象一个真实世界中应用监督学习的场景，你期望预测的结果是什么？举出两个在这个场景中能够帮助你进行预测的数据集中的特征。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**回答**: *商品推荐预测： 商品适用人群（男/女），适用年龄，职位，平均月工资，用户画像信息*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **注意**: 当你写完了所有**5个问题，3个TODO**。你就可以把你的 iPython Notebook 导出成 HTML 文件。你可以在菜单栏，这样导出**File -> Download as -> HTML (.html)** 把这个 HTML 和这个 iPython notebook 一起做为你的作业提交。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "翻译：毛礼建 ｜ 校译：黄强 ｜ 审译：曹晨巍"
   ]
  }
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